A Batch Postprocessing Method Based on an Adaptive Data Partitioning Strategy for DEM Differencing From Global Data Products

The digital elevation model (DEM) differencing method has been widely used for quantifying land surface change, especially in the field of glacier mass change. However, when the global DEM datasets are used to estimate large-scale glacier changes, the superposition effect of multisource errors makes...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5
Main Authors: Zhou, Yushan, Duan, Meng
Format: Article
Language:English
Subjects:
C band
Digital Elevation Models
Elevation
Errors
Glaciers
Partitioning
Plateaus
Radar
River valleys
Spatial data
Superhigh frequencies
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Summary:The digital elevation model (DEM) differencing method has been widely used for quantifying land surface change, especially in the field of glacier mass change. However, when the global DEM datasets are used to estimate large-scale glacier changes, the superposition effect of multisource errors makes it difficult to remove the errors in the differencing maps using the existing method based on manually supervised correction. In this letter, to solve this issue, we propose a new stepwise correction method with an adaptive data partitioning strategy. The first step is to perform global spatial detrending, followed by global coarse co-registration. Subsequently, an initial data partitioning scheme based on the quadtree partitioning technique and relying on terrain slope is proposed. Furthermore, a parameter, i.e., the standardized standard deviation (SSTD) based on elevation changes, is used to further update the partitioning scheme. Finally, for each segmented data block, local spatial detrending is performed, followed by precise local co-registration. The proposed method was tested in the Pamir Plateau and the Tianshan River valley, based on the Shuttle Radar Topography Mission (SRTM) C-band and X-band DEMs and the Copernicus DEM. The final results show that the various types of error can be automatically and effectively removed, and that the normalized median absolute deviation (NMAD) and long-range correlation variance in ice-free areas show an improvement of 10% and 24% and 37% and 60% for the two study regions, respectively, demonstrating the good performance of the proposed method.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3365067